Spatial Data Science
Preface
I Spatial Data
1
Getting Started
1.1
A first map
1.2
Projection and coordinate reference systems
1.3
Raster and vector data
1.4
Raster types
1.5
Time series, arrays, data cubes
1.6
Support, attribute-geometry relationships
1.7
The Spatial Data Science software ecosystem
1.8
Exercises
2
Coordinate systems
2.1
Cartesian and geodetic coordinates
2.2
Ellipsoidal coordinates
2.3
Distances
2.4
Bounded spaces
2.5
Time
2.6
Exercises
3
Geometries
3.1
Simple feature geometry types
3.2
Simple features in
sf
3.3
Tesselations: coverages, rasters
3.4
Networks
3.5
Geometries on the sphere
4
Raster and vector datacubes
4.1
Package
stars
4.2
Raster data
4.3
Vector Datacubes
4.4
Exercises
5
Manipulating Geometries
5.1
Predicates
5.2
Measures
5.3
Geometry generating functions
5.4
Precision
5.5
Generating invalid geometries
5.6
Warnings for longitude/latitude geometries
6
Feature attributes
6.1
Attribute-geometry relationships
6.2
Spatial join
6.3
Aggregate and summarise
6.4
Intersections
6.5
Area-weighted interpolation
6.6
Exercises
7
Reference Systems
7.1
Units of measurement
7.2
Temporal Reference Systems
7.3
Coordinate Reference Systems
II Maps
8
Plotting spatial data
8.1
Every plot is a projection
8.2
Plotting points, lines, polygons, grid cells
8.3
Color palettes and class intervals
8.4
Poles and datelines
8.5
Graticules and other navigation aids
9
Base and grid plots
9.1
Base plots
9.2
Combining base plots
9.3
Grid plots and viewports
10
ggplot2
10.1
geom_sf
10.2
geom_stars
11
Interactive Maps
III Spatial Analysis
12
Summarizing Geometries
13
Point Pattern Analysis
14
Manipulating attributes: summarise, aggregate, union, sample
15
Up- and Downscaling
16
Interpolation and Geostatistics
17
Area Data and Spatial Autocorrelation
17.1
Spatial autocorrelation
17.2
Spatial weight matrices
17.3
Measures of spatial autocorrelation
17.4
Spatial heterogeneity
18
Spatial Regression
18.1
Spatial regression with spatial weights
18.2
Estimators
18.3
Implementation details
18.4
Markov random field and multilevel models with spatial weights
19
Movement data
20
Statistical modelling of spatiotemporal data
21
sp and raster
21.1
links and differences between sf and sp
21.2
migration packages
21.3
raster, stars and sf
R basics
21.4
Pipes
21.5
Data structures
References
Published with bookdown
Spatial Data Science
Chapter 16
Interpolation and Geostatistics